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All Journal Journal of Applied Information, Communication and Technology (JAICT) IC Tech: Majalah Ilmiah
M Adib Al Karomi
STMIK Widya Pratama
Computer Science & IT Economics, Econometrics & Finance Engineering

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Strengthening campus finance by analyzing attribute attributes for student registration classifications M Adib Al Karomi; Much. Rifqi Maulana; Slamet Joko Prasetiono; Ivandari Ivandari; Arochman Arochman
JAICT Vol 4, No 2 (2019)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (206.394 KB) | DOI: 10.32497/jaict.v4i2.1431

Abstract

Students are the most valuable assets in a private college. Assets like this that really need to be maintained and maintained, because most of the income from the private campus is derived from the tuition fees of students. The large number of students who resigned and did not conduct registration would have an impact on the financial institutions. STMIK Widya Pratama is the only computer science campus in Pekalongan City. Data from the last 5 years obtained from the new student admissions committee at STMIK Widya Pratama Pekalongan shows that out of 2670 prospective students who enroll, there are at least 514 prospective students who do not register. This means that around 20% of students do not register. Several analyzes related to the classification for student registration were conducted. In this case the best method that can be used is C45. In the process of calculating the C45 algorithm, information gain method is used to determine the importance of data attributes. The calculation results show that the attribute with the highest level of importance is the city_district attribute from the prospective student's residence, followed by the attributes of education, parental education, and tuition. These results can later be used and developed to create a system to support campus policy.
Fuzzy Integration to Standard Calculation of K-Nearest Neighbour Attributes M Adib Al Karomi; Ivandari Ivandari
JAICT Vol 5, No 2 (2020)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v5i2.1984

Abstract

The development of information and data in the era of the industrial revolution 4.0 is very fast. Researchers, institutions and even industry are competing to find and utilize methods in data processing that are more effective and efficient. In data mining classification, there are several best methods and are widely used by researchers. One of them is K-Nearest Neighbor (KNN). The calculation process in the KNN algorithm is carried out by comparing the testing data to all existing training data. This comparison is generally symbolized by the value of closeness or similarity between attribute records. The KNN method is proven to be good for handling large datasets and datasets with many attributes. One of the drawbacks in calculating the similarity of the KNN is that if there are attributes with a large range value, the similarity value will also be large. Conversely, if the range in an attribute is small, the similarity is also small. This condition is clearly unfair considering the types of attributes in the current data vary widely. One solution to this problem is to use standardization for all existing data attributes. Fuzzy is a model introduced by Prof. Zadeh which allows a faint value to be a value between 1 and 0. In this study the fuzzy model will be integrated in the KNN similarity calculation to obtain standardization of all data attributes. The results show that the use of the KNN algorithm in the classification of credit approval has an accuracy rate of 91.83%.
Improved C45 performance with gain ratio for credit approval dataset Ivandari Ivandari; M Adib Al Karomi; Much. Rifqi Maulana
JAICT Vol 7, No 2 (2022)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v7i2.3978

Abstract

Abstract— People's shopping behavior has undergone many changes after the COVID-19 pandemic. Many people have switched to using the marketplace to make buying and selling transactions. The payment process in the marketplace is relatively easy, especially when using a credit card. The increase in demand for credit must be addressed better by financial providers to minimize bad loans. The best thing in minimizing bad credit is to be more selective in choosing credit customers. Data mining is a field that can study old data to become new knowledge in the future. In data mining, the classification of bad credit customers is mostly done. One of the algorithms that excels in handling credit approval datasets is C45. The C45 model is widely used because it has an output decision tree that is easier to understand in human language. The number of data attributes can affect the performance of the algorithm. Feature selection is a form of attribute reduction to improve data quality and improve classification algorithm performance. Gain ratio is the development of information gain and is the best feature selection model and is widely used by researchers. This study performs a classification using C45 and uses a gain ratio for the selection of credit approval data features. By using the gain ratio, the accuracy of the C45 classification algorithm increased from the previous 94.12% to 95.29%.
Aplikasi Pendukung Keputusan Persetujuan Kredit berbasis WEB dengan Pemanfaatan Algoritma Data Mining M Adib Al Karomi; Christian Yulianto Rusli
IC-Tech Vol 13 No 1 (2018): IC-Tech Volume XIII No.1 April 2018
Publisher : STMIK WIDYA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (251.07 KB) | DOI: 10.47775/ictech.v13i1.21

Abstract

Dalam kehidupan modern saat ini banyak masyarakat menginginkan kepemilikan atas suatu barang dengan jasa pinjaman dana atau lebih dikenal dengan istilah kredit. Kondisi seperti ini memungkinkan berkembangnya perusahaan jasa keuangan dengan berbagai macam penawaran pembiayaan untuk barang impian dari nasabah. Sayangnya dari hasil penelitian sebelumnya banyak nasabah tergolong dalam klasifikasi kredit macet. Hal ini membuat berbagai perusahaan jasa keuangan berpikir keras untuk mengurangi kerugian atas banyaknya kredit macet. Salah satu penanggulangan awal yang dapat dilakukan adalah dengan melakukan klasifikasi calon nasabah menggunakan sebuah perhitungan algoritmik dengan perbandingan nasabah yang pernah tercatat sebelumnya. Beberapa  model klasifikasi banyak digunakan. Salah satu yang terbaik adalah menggunakan metode naive bayes. Metode ini memungkinkan perhitungan probabilitas dari setiap atribut yang adaelitian ini menciptakan sebuah aplikasi pendukung keputusan persetujuan kredit dengan menggunakan algoritma naive bayes. Hsistem dapat menjadi pendukuputusan atas persetujuan pemberian kredit terhadap nasabah. Sistem ini tidak mengikat hasil akhir klasifikasi untuk pembiayaan nasabah karena keputusan akhir adalah hak dari manajerial perusahaan penyedia pembiayaan.
Fuzzifikasi Data untuk Normalisasi Atribut dalam Perhitungan Algoritma K-Nearest Neighbour M Adib Al Karomi
IC-Tech Vol 13 No 2 (2018): IC-Tech Volume XIII No.2 Oktober 2018
Publisher : STMIK WIDYA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (721.839 KB) | DOI: 10.47775/ictech.v13i2.41

Abstract

K-Nearest Neighbour Merupakan algoritma yang sering digunakan dalam proses klasifikasi. Dalam proses perhitungannya algoritma ini menggunakan pendekatan similarity antar record atribut. Fungsi ini terbukti baik digunakan dan dapat menghasilkan klasifikasi yang cukup akurat. Kelemahan pendekatan similarity ini adalah apabila terdapat atribut dengan range nilai yang berbeda jauh maka akan menghasilkan nilai similarity yang besar. Nilai ini jelas tidak adil apabila terdapat atribut lain yang memiliki range sangat kecil. Perhitungan menggunakan fuzzy dinilai sangat cocok untuk menangani masalah ini. Dalam perhitungan fuzzy digunakan nilai terbesar yaitu 1 dengan nilai terendah adalah 0. Penelitian ini melakukan perhitungan algoritma K-Nearest Neighbour menggunakan fuzzy dan dilakukan perbandingan dengan perhitungan tanpa menggunakan fuzzifikasi data. Hasil dari penelitian ini membuktikan bahwa fuzzifikasi data untuk normalisasi atribut dapat membuat perhitungan klasifikasi k-nearest neighbor lebih akurat dan sesuai dengan sasaran.
Co-Authors Arochman Arochman Christian Yulianto Rusli Ivandari Ivandari Much. Rifqi Maulana Slamet Joko Prasetiono